Try updating result_type for array API

Author: dcherian

xarray/core/dtypes.py

@@ -168,6 +168,7 @@ def is_datetime_like(dtype):
 
 def result_type(
     *arrays_and_dtypes: np.typing.ArrayLike | np.typing.DTypeLike,
+    xp=np,
 ) -> np.dtype:
     """Like np.result_type, but with type promotion rules matching pandas.
 
@@ -184,12 +185,13 @@ def result_type(
     -------
     numpy.dtype for the result.
     """
-    types = {np.result_type(t).type for t in arrays_and_dtypes}
+    types = {xp.result_type(t) for t in arrays_and_dtypes}
 
-    for left, right in PROMOTE_TO_OBJECT:
-        if any(issubclass(t, left) for t in types) and any(
-            issubclass(t, right) for t in types
-        ):
-            return np.dtype(object)
+    if xp == np:
+        for left, right in PROMOTE_TO_OBJECT:
+            if any(issubclass(t.type, left) for t in types) and any(
+                issubclass(t.type, right) for t in types
+            ):
+                return np.dtype(object)
 
-    return np.result_type(*arrays_and_dtypes)
+    return xp.result_type(*arrays_and_dtypes)

xarray/core/duck_array_ops.py

@@ -237,7 +237,7 @@ def as_shared_dtype(scalars_or_arrays, xp=np):
     # get handled properly.
     # Note that result_type() safely gets the dtype from dask arrays without
     # evaluating them.
-    out_type = dtypes.result_type(*arrays)
+    out_type = dtypes.result_type(*arrays, xp=xp)
     return [
         astype(x, out_type, copy=False) if hasattr(x, "dtype") else x for x in arrays
     ]